Search Results for author: Dallas Card

Found 21 papers, 14 papers with code

Expected Validation Performance and Estimation of a Random Variable’s Maximum

no code implementations Findings (EMNLP) 2021 Jesse Dodge, Suchin Gururangan, Dallas Card, Roy Schwartz, Noah A. Smith

We find that the two biased estimators lead to the fewest incorrect conclusions, which hints at the importance of minimizing variance and MSE.

Substitution-based Semantic Change Detection using Contextual Embeddings

1 code implementation5 Sep 2023 Dallas Card

Measuring semantic change has thus far remained a task where methods using contextual embeddings have struggled to improve upon simpler techniques relying only on static word vectors.

Change Detection

Problems with Cosine as a Measure of Embedding Similarity for High Frequency Words

1 code implementation ACL 2022 Kaitlyn Zhou, Kawin Ethayarajh, Dallas Card, Dan Jurafsky

Cosine similarity of contextual embeddings is used in many NLP tasks (e. g., QA, IR, MT) and metrics (e. g., BERTScore).

Modular Domain Adaptation

1 code implementation Findings (ACL) 2022 Junshen K. Chen, Dallas Card, Dan Jurafsky

Off-the-shelf models are widely used by computational social science researchers to measure properties of text, such as sentiment.

Domain Adaptation text-classification +1

Expected Validation Performance and Estimation of a Random Variable's Maximum

no code implementations1 Oct 2021 Jesse Dodge, Suchin Gururangan, Dallas Card, Roy Schwartz, Noah A. Smith

We find that the two biased estimators lead to the fewest incorrect conclusions, which hints at the importance of minimizing variance and MSE.

On the Opportunities and Risks of Foundation Models

3 code implementations16 Aug 2021 Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S. Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, Erik Brynjolfsson, Shyamal Buch, Dallas Card, Rodrigo Castellon, Niladri Chatterji, Annie Chen, Kathleen Creel, Jared Quincy Davis, Dora Demszky, Chris Donahue, Moussa Doumbouya, Esin Durmus, Stefano Ermon, John Etchemendy, Kawin Ethayarajh, Li Fei-Fei, Chelsea Finn, Trevor Gale, Lauren Gillespie, Karan Goel, Noah Goodman, Shelby Grossman, Neel Guha, Tatsunori Hashimoto, Peter Henderson, John Hewitt, Daniel E. Ho, Jenny Hong, Kyle Hsu, Jing Huang, Thomas Icard, Saahil Jain, Dan Jurafsky, Pratyusha Kalluri, Siddharth Karamcheti, Geoff Keeling, Fereshte Khani, Omar Khattab, Pang Wei Koh, Mark Krass, Ranjay Krishna, Rohith Kuditipudi, Ananya Kumar, Faisal Ladhak, Mina Lee, Tony Lee, Jure Leskovec, Isabelle Levent, Xiang Lisa Li, Xuechen Li, Tengyu Ma, Ali Malik, Christopher D. Manning, Suvir Mirchandani, Eric Mitchell, Zanele Munyikwa, Suraj Nair, Avanika Narayan, Deepak Narayanan, Ben Newman, Allen Nie, Juan Carlos Niebles, Hamed Nilforoshan, Julian Nyarko, Giray Ogut, Laurel Orr, Isabel Papadimitriou, Joon Sung Park, Chris Piech, Eva Portelance, Christopher Potts, aditi raghunathan, Rob Reich, Hongyu Ren, Frieda Rong, Yusuf Roohani, Camilo Ruiz, Jack Ryan, Christopher Ré, Dorsa Sadigh, Shiori Sagawa, Keshav Santhanam, Andy Shih, Krishnan Srinivasan, Alex Tamkin, Rohan Taori, Armin W. Thomas, Florian Tramèr, Rose E. Wang, William Wang, Bohan Wu, Jiajun Wu, Yuhuai Wu, Sang Michael Xie, Michihiro Yasunaga, Jiaxuan You, Matei Zaharia, Michael Zhang, Tianyi Zhang, Xikun Zhang, Yuhui Zhang, Lucia Zheng, Kaitlyn Zhou, Percy Liang

AI is undergoing a paradigm shift with the rise of models (e. g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.

Transfer Learning

The Values Encoded in Machine Learning Research

1 code implementation NeurIPS 2021 Abeba Birhane, Pratyusha Kalluri, Dallas Card, William Agnew, Ravit Dotan, Michelle Bao

We present extensive textual evidence and identify key themes in the definitions and operationalization of these values.

BIG-bench Machine Learning

Detecting Stance in Media on Global Warming

1 code implementation Findings of the Association for Computational Linguistics 2020 Yiwei Luo, Dallas Card, Dan Jurafsky

We release our stance dataset, model, and lexicons of framing devices for future work on opinion-framing and the automatic detection of GW stance.

Causal Effects of Linguistic Properties

1 code implementation NAACL 2021 Reid Pryzant, Dallas Card, Dan Jurafsky, Victor Veitch, Dhanya Sridhar

Second, in practice, we only have access to noisy proxies for the linguistic properties of interest -- e. g., predictions from classifiers and lexicons.

Language Modelling

With Little Power Comes Great Responsibility

2 code implementations EMNLP 2020 Dallas Card, Peter Henderson, Urvashi Khandelwal, Robin Jia, Kyle Mahowald, Dan Jurafsky

Despite its importance to experimental design, statistical power (the probability that, given a real effect, an experiment will reject the null hypothesis) has largely been ignored by the NLP community.

Experimental Design Machine Translation +1

On Consequentialism and Fairness

no code implementations2 Jan 2020 Dallas Card, Noah A. Smith

In this paper we provide a consequentialist critique of common definitions of fairness within machine learning, as well as a machine learning perspective on consequentialism.

BIG-bench Machine Learning Decision Making +2

Show Your Work: Improved Reporting of Experimental Results

4 code implementations IJCNLP 2019 Jesse Dodge, Suchin Gururangan, Dallas Card, Roy Schwartz, Noah A. Smith

Research in natural language processing proceeds, in part, by demonstrating that new models achieve superior performance (e. g., accuracy) on held-out test data, compared to previous results.

The Risk of Racial Bias in Hate Speech Detection

no code implementations ACL 2019 Maarten Sap, Dallas Card, Saadia Gabriel, Yejin Choi, Noah A. Smith

We investigate how annotators{'} insensitivity to differences in dialect can lead to racial bias in automatic hate speech detection models, potentially amplifying harm against minority populations.

Hate Speech Detection

Deep Weighted Averaging Classifiers

2 code implementations6 Nov 2018 Dallas Card, Michael Zhang, Noah A. Smith

Recent advances in deep learning have achieved impressive gains in classification accuracy on a variety of types of data, including images and text.

General Classification

The Importance of Calibration for Estimating Proportions from Annotations

no code implementations NAACL 2018 Dallas Card, Noah A. Smith

Estimating label proportions in a target corpus is a type of measurement that is useful for answering certain types of social-scientific questions.

Sentiment Analysis Text Categorization

Neural Models for Documents with Metadata

3 code implementations ACL 2018 Dallas Card, Chenhao Tan, Noah A. Smith

Most real-world document collections involve various types of metadata, such as author, source, and date, and yet the most commonly-used approaches to modeling text corpora ignore this information.

Topic Models Variational Inference

Friendships, Rivalries, and Trysts: Characterizing Relations between Ideas in Texts

1 code implementation ACL 2017 Chenhao Tan, Dallas Card, Noah A. Smith

Combining two statistics --- cooccurrence within documents and prevalence correlation over time --- our approach reveals a number of different ways in which ideas can cooperate and compete.

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